Some remarks on the agricultural production model in the European Union.

Dogaru, Vasile ; Duda Daianu, Dana Codruta

Abstract: In the context of the European Union's enlargement, the maintenance/increase of the farming performance becomes a matter of utmost importance. The reduction of the food product weight in the consumption basket--an important index when measuring the economic development of a country -, directly influenced by farming sector competitiveness, and the agricultural issue are reference points to the Single Market. Comparison with the American or global farming performances keeps being a constant concern in the next decade. Thus, modelling the influence of the agricultural production factors represents a basic premise of analytical study. In this way, one can state some limited remarks for the improvement of the European model of farming.

Key words: competitiveness, agriculture, Roegenian modelling, Common Agricultural Policy

1. INTRODUCTION

The target of the systematic increase of the EU's competitiveness in the next decade has as a direction of study the Common Agriculture Policy. Besides the so-called agricultural issue, one of the basic problems of this sector is competitiveness of farming products, which will further influence the size of this indicator in other sectors. The agricultural issue refers to the need of finding solutions for the redistribution of labour force, once it is being drawn out from farming sector as total productivity is getting higher (Eatwell, 1987).

One of the performance indexes of an economy, taken in consideration more and more, is the weight of food products in the people's consumption basket. As the weight of food products, and especially in enlargement context, vacillates around an average of about 20% for UE-27, it is necessary to study the production factors of the food sector. Moreover, in some New Member Countries, this weight exceeds 30%, likely to its value from some countries in The Commonwealth of Independent States. In the USA, during the last two and half decades, the food product weight in the consumption basket decreased from 14% to about 7%.

Some studies (Dogaru, 2003) have identified different models of agricultural production development, for groups of countries, according to their level of income (gross domestic product). These models changed significantly during the two last decades of last century (1980-1998). Consequently, it is further necessary to study the production model for a group of countries with relative homogenous economic development. The issues related to food security that past Europe did not know, face some Eastern countries now (Ben-David, 1999) and possibly in the future, requiring a more systematic study of the farming sector efficiency. As a matter of fact, farming and mining provide resources for all other economic sectors (Georgescu-Roegen, 1971), either economists and other experts do admit or do not.

A high pressure regarding consumption will keep acting in the next decades (World Resources Institute, 2000) on available resources which are in relative decrease because of larger and larger exploitation rates. Some more systematic studies reveal, from this prospect, certain farming problems which tend to affect the food sector too (Alexandratos, 1995).

2. RESEARCH COURSE

The study was carried out on the basis of information that concerns the production factors and the level of agricultural production, from the data base of Food and Agriculture Organization (FAO, 2007). The lack of other information on production factors of other products, as well as limits imposed by the study length, restrained the research to the cereal production only.

The main reason for using the electronic FAO data base is the fact that studies made by World Bank and other international organizations generally use this data base for information on farming. Information taken from FAO data bases was analysed in the following steps:

--Establishing the production factors to be considered in the analysis, i.e. agricultural population (thousands of people), agricultural area used (thousands of miles), agricultural equipments (tractors), fertilisers used (tons), agricultural area irrigated (thousands of miles);

--Selection of data regarding production factors and cereal production for the Member States (UE 27), for 1980, 1990, 1995, 2000 and 2003;

The data referring to Luxemburg and Belgium are cumulated within FAO data base. For the other countries, due values for production factors and cereal production already existed or it was possible to calculate them separately. New countries having come into existence after 1990, as a result of dividing the Ex-Soviet Union, as for instance Latvia, Estonia and Lithuania, or some countries from the former Yugoslavia (Slovenia), or from the Central Europe (Czech Republic and Slovakia), made necessary to find suitable solutions. Consequently, either data from the most recent period have been used or (simple) extrapolation has been involved. In agricultural population's case, existing data only for 1980, 1990 and 2000 were extrapolated for the other years, by the method of average rate.

The missing data for the Check Republic and Slovakia were calculated by taking the data for Czechoslovakia as a reference point. The proportionality principle was used, percentages of structure from the nearest period being applied to the concerned value. The suggested regressive relation between the cereal production and the main production factors is as follows:

[P.sub.c] = [[beta].sub.1] x [P.sub.a] + [[beta].sub.2] x [P.sub.m] + [[beta].sub.3] x [T.sub.r] + [[beta].sub.4] x [I.sub.n] + [[beta].sub.5] x [I.sub.r] (1)

where: Pc is the cereal production, Pa is the agricultural population, Pm quantifies the arable land areas with permanent crops, Tr represents the tractor utilities, In is the fertiliser quantity administrated, and Ir the irrigated area. [[beta].sub.1], [[beta].sub.2], [[beta].sub.3] [[beta].sub.4], [[beta].sub.5] coefficients correspond to these variables, and is the constant.

The choice of the regression relation considered some assumptions from methodological research of last decades. Consequently, there has been chosen a simple model, which would mathematically and literally describe simultaneously the observed reality. We had in view the impossibility of seizing the trend exactly and using in a rigorous manner the result of the model to calculate the further development of the sector, because of the qualitative leap. The use of a simple model was the option of some famous economists, who had a deep insight in Mathematics, as J. M. Keynes, S. Kuznets, J. Tobin and N. Georgescu-Roegen. Moreover, according to Georgescu-Roegen (1971), proper (re)organization of information concerning the agricultural production factors allows us the passage of the most difficult statistical tests.

The study of inputs in farming has taken as a starting point the finding that farming efficiency differed in various countries, according to the endowment with production factors.

The analyse we achieved at EU-27 level was preceded by /also includes/ comparison of results of the four regressions carried out for the three groups of identified countries (at the level of the years 1980, 1990, 1995 and 2003), according to the income. The first group of countries includes 8 states, Bulgaria, Romania, Poland, Latvia, Lithuania, Slovakia, Hungary, Estonia, with a GDP per capita (PPS) ranging between 35-70% out of 100% of the EU-25;. The second group includes Portugal, Malta, Czech Republic, Slovenia, Greece, Cyprus, Spain and Italy with values ranging between 70%-105%, while the third consider France, Germany, Finland, United Kingdom, Sweden, Belgium, Denmark, Austria, Netherlands, Ireland and Luxembourg, with values of 105%-240%. The article publishes only the results concerning 2003. They do not differ significantly from those obtained for previous years.

The results obtained through regressive calculations for the 3 groups of states and, globally, for the EU-27, for 2003, are presented in Table 1.

The relation between the cereal production and the five production factors for 2003 is satisfactorily seized for EU-27, R2 being 0.785, and by 25% higher for every country from the three groups. The F test is passed for all four regressions at signification level of 0.05.

At the same signification level, the test t-Student is passed for the coefficients of the variables 'population' and 'agricultural irrigated land' in the first and third groups of countries, and on constant for all the regressions too. On the other variables, the test t-Student is not passed.

During the period 1980-1994, EU-27 cereal production raised, while during 1995-2003 there was a decrease of 10.1%. For the first group of states, in the period 1990-2003, there was identified a decrease in the factors 'agricultural population' (-32%) and 'agricultural irrigated land' (-16%). One can observe the growing importance of technological progress implementation for first-group states, while for the secondgroup states there was identified an increasing importance of agriculture irrigated land.

The Common Agricultural Policy aims at competitiveness increasing in the EU farming, through implementation of technical progress and optimal use of production factors, especially the working force. Internal and external competitiveness increase is intended to help European agricultural producers to adjust to developments of foodproduct global market.

3. CONCLUSION

The deduced models partially justify the expression "Common Agricultural Policy" within the Single Market. The choice of a simple model was based on some remarks of experts of the last half-century. It enabled us to identify some common tendencies inside this sector, for the Member States arranged in three groups. As the countries concerned by our analyse belong to the same geographic and economic area, the further study can be extended to other important agricultural and non-agricultural products too.

4. REFERENCES

Alexandratos, Nikos (1995). World Agriculture. Towards 2010, Food and Agriculture Organization of the United Nations and John Wiley & Sons, Chichester, ISBNs 0-471-9537-8, 92-5-103590-3, New York, Brisbane -Toronto, Singapore

Ben-David, D; Nordstrom, H; Winters, L. (1999). Trade Income Disparity and Poverty, Geneva--World Trade Organisation

Dogaru, V. (2003). The Population Income and the Prices of Agri-Food Products. Comparative analysis of countries trend between 1950-2002, Expert Publishing House, ISBN 973-8177-68-5, Bucharest

Eatwell, J. and others, editors (1987). The New Palgrave. A Dictionary of Economics, tome I-IV, The Macmillan Press Limited, ISBN 0-333-372352, London

Food and Agricultural Organisation (FAO) (2007). FAOStat Agriculture Data http://faostat.fao.org/site/339/default. aspx accesed 2007-08-14

Georgescu-Roegen, N. (1971). The Entropy Law and the Economic Process, Harvard University Press, ISBN 674-25780-4, Cambridge, Massachusetts

World Resources Institute. (2000). The Weight of Nations, Material Outflows from Industrial Economies, ISBN-13: 978-1569734391, New York.

Table 1. The regression of cereal production, in accordance with
farming inputs, 3 groups of countries and EU-27, 2003.

 R1-EU27 R2-Gr 1 R3-Gr 2 R4-Gr 3

[[beta].sub.1] -1.382 8.962 2.162 7.130
 (1,443) (4.603) (0.874) (14.688)
[[beta].sub.2] 0.271 0.881 -1.363 0.050
 (0.131) (0.610) (0.466) (0.122)
[[beta].sub.3] 0.012 -0.043 0.019 0.026
 (0.005) (0.027) (0.003) (0.021)
[[beta].sub.4] 0.004 -0.001 0.039 0.000
 (0.001) (0.003) (0.009) (0.003)
[[beta].sub.5] 0.545 -6.0386 1.3249 4.2058
 (1.639) (4.364) (1.4813) (4.3468)
[[beta].sub.0] -1380.23 -4123 665 -3891
 (2034.69) (3439) (486) (2626)
R2 0.785 0.9807 0.9982 0.9700
F 14.63 20.37 224.63 25.88
ESE (thousands) 3465458 427450 502917 3005492

Source: FAO, 2007. Note: R 1, 2, 3 and 4 - 1, 2, 3 and 4 regression;
Gr. 1, 2, 3 and EU-27 - 1, 2, 3 group and EU-27; own calculations; the
figures between brackets are the t Student values of the coefficients.
Critical values (1 regression, UE-27): [F.sub.0.05 ;5 ;20] test = 2.7;
t [Student.sub.0,05 ;20] = 2.09; Critical values (II and III
regression): [F.sub.0.05 ; 5 ;2] test = 19.3; t [Student.sub.0,05 ;2]
= 4.30; Critical values (IV regression): [F.sub.0.05 ; 5 ;4] test =
6.3; t [Student.sub.0,05 ;4] = 2.78, ESE - Error standard estimation.